Heterogeneous Data Management, Polystores, and Analytics for Healthcare

VLDB Workshops, Poly 2022 and DMAH 2022, Virtual Event, September 9, 2022, Revised Selected Papers
Herausgegeben:Rezig, El Kindi; Gadepally, Vijay; Mattson, Timothy; Stonebraker, Michael; Kraska, Tim; Kong, Jun; Luo, Gang; Teng, Dejun; Wang, Fusheng

• Broschiertes Buch

Produktbeschreibung

This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2022, and the 8th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2022, which were held virtually on September 9, 2022.

The proceedings include 3 full papers each from Poly 2022 and from DMAH 2022.
DMAH deals with innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare, public health, and everyday wellness, with clinical, physiological, imaging, behavioral, environmental, and omic - data, and data from social media and the Web.

Poly is focusing on the broader real-world polystore problem, which includes data management, data integration, data curation, privacy, and security.

Produktdetails

• Lecture Notes in Computer Science 13814
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-23904-5
• 1st ed. 2022
• Seitenzahl: 108
• Erscheinungstermin: 21. Januar 2023
• Englisch
• Abmessung: 235mm x 155mm x 7mm
• Gewicht: 178g
• ISBN-13: 9783031239045
• ISBN-10: 3031239040
• Artikelnr.: 66446014

Inhaltsangabe

POLY 2022.- Privacy, Security and/or Policy Issues for Heterogeneous Data.- Ad-hoc Searches on Image Databases.- A Survey of Data Challenges across a Modernizing Bureaucracy: A New Perspective on Examining Old Government Problems.- Purpose Scan: A purpose-aware access method.- DMAH 2022.- Enabling Real-world Medicine with Data Lake Federation: a research perspective.- Towards Assessing Data Bias in Clinical Trials.- Clinical synthetic data generation to predict and identify risk factors for cardiovascular diseases.